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We describe a novel pipeline to automatically discover hierarchies ofrepeated sections in musical audio. The proposed method uses similarity networkfusion (SNF) to combine different frame-level features into clean affinitymatrices, which are then used as input to spectral clustering. While priorspectral clustering approaches to music structure analysis have pre-processedaffinity matrices with heuristics specifically designed for this task, we showthat the SNF approach directly yields segmentations which agree better withhuman annotators, as measured by the ``L-measure'' metric for hierarchicalannotations. Furthermore, the SNF approach immediately supports arbitrarilymany input features, allowing us to simultaneously discover structure encodedin timbral, harmonic, and rhythmic representations without any changes to thebase algorithm.

Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion